The impact of information sharing on bullwhip effect reduction in a supply chain

PurposeInformation distortion results in demand variance amplification in upstream supply chain members, known as the bullwhip effect, and inventory inaccuracy in the inventory records. As inventory inaccuracy contributes to the bullwhip effect, the purpose of this paper is to investigate the impact of inventory inaccuracy on the bullwhip effect in radio-frequency identification (RFID)-enabled supply chains and, in this context, to evaluate supply chain performance because of the RFID technology.Design/methodology/approachA simulation modeling method based on hierarchical timed colored petri nets is presented to model inventory management in multi-stage serial supply chains subject to inventory inaccuracy for various traditional and information sharing configurations in the presence and absence of RFID. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results.FindingsThe bullwhip effect is increased in RFID-enabled multi-stage serial supply chains subject to inventory inaccuracy. The information sharing supply chain is more sensitive to the impact of inventory inaccuracy.Research limitations/implicationsInformation sharing involves collaboration in market demand and inventory inaccuracy, whereas RFID is implemented by all echelons. To obtain the full benefits of RFID adoption and collaboration, different collaboration strategies should be investigated.Originality/valueColored petri nets simulation modeling of the inventory management process is a novel approach to study supply chain dynamics. In the context of inventory errors, information on RFID impact on the dynamic behavior of multi-stage serial supply chains is provided.

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Exploring the Bullwhip Effect and Inventory Stability in a Seasonal Supply Chain

International Journal of Engineering Business Management ◽

10.5772/56833 ◽

2013 ◽

Vol 5 ◽

pp. 23 ◽

Cited By ~ 11

Author(s):

Francesco Costantino ◽

Giulio Di Gravio ◽

Ahmed Shaban ◽

Massimo Tronci

Keyword(s):

Supply Chain ◽

Information Sharing ◽

Performance Measures ◽

Moving Average ◽

Bullwhip Effect ◽

Fill Rate ◽

Supply Chain Dynamics ◽

Effect Ratio ◽

The Impact ◽

Demand Seasonality

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.

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Unraveling Behavioral Ordering: Relative Costs and the Bullwhip Effect

Manufacturing & Service Operations Management ◽

10.1287/msom.2021.1030 ◽

2021 ◽

Author(s):

Brent B. Moritz ◽

Arunachalam Narayanan ◽

Chris Parker

Keyword(s):

Supply Chain ◽

Information Sharing ◽

Human Behavior ◽

Bullwhip Effect ◽

Problem Definition ◽

Supply Chain Costs ◽

Cost Impact ◽

Managerial Implications ◽

The Cost ◽

The Impact

Problem definition: We study the bullwhip effect and analyze the impact of human behavior. We separate rational ordering in response to increasing incoming orders from irrational ordering. Academic/practical relevance: Prior research has shown that the bullwhip effect occurs in about two-thirds of firms and impacts profitability by 10%–30%. Most bullwhip mitigation efforts emphasize processes such as information sharing, collaboration, and coordination. Previous work has not been able to separate the impact of behavioral ordering from rational increases in order quantities. Methodology: Using data from a laboratory experiment, we estimate behavioral parameters from three ordering models. We use a simulation to evaluate the cost impact of bullwhip behavior on the supply chain and by echelon. Results: We find that cost increases are not equally shared. Human biases (behavioral ordering) at the retailer results in higher relative costs elsewhere in the supply chain, even as similar ordering by a wholesaler, distributor, or factory results in increased costs within that echelon. These results are consistent regardless of the behavioral models that we consider. The cognitive profile of the decision maker impacts both echelon and supply chain costs. We show that the cost impact is higher as more decision makers enter a supply chain. Managerial implications: The cost of behavioral ordering is not consistent across the supply chain. Managers can use the estimation/simulation framework to analyze the impact of human behavior in their supply chains and evaluate improvement efforts such as coordination or information sharing. Our results show that behavioral ordering by a retailer has an out-sized impact on supply chain costs, which suggests that upstream echelons are better placed to make forecasting and replenishment decisions.

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Information Sharing in Supply Chain Management with Demand Uncertainty

Advances in Information Resources Management - Advanced Topics in Information Resources Management, Volume 5 ◽

10.4018/978-1-59140-929-8.ch003 ◽

2011 ◽

pp. 44-62 ◽

Cited By ~ 3

Author(s):

Zhensen Huang ◽

Aryya Gangopadhyay

Keyword(s):

Supply Chain ◽

Information Sharing ◽

Demand Uncertainty ◽

Bullwhip Effect ◽

Supply Chain Performance ◽

Chain Model ◽

Simulation Approach ◽

Supply Chain Model ◽

Trading Partners ◽

The Impact

Information sharing is a major strategy to counteract the amplification of demand fluctuation going up the supply chain, known as the bullwhip effect. However, sharing information through interorganizational channels can raise concerns for business management from both technical and commercial perspectives. The existing literature focuses on examining the value of information sharing in specific problem environments with somewhat simplified supply chain models. The present study takes a simulation approach in investigating the impact of information sharing among trading partners on supply chain performance in a comprehensive supply chain model that consists of multiple stages of trading partners and multiple players at each stage.

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Demand Sharing Inaccuracies in Supply Chains: A Simulation Study

Complexity ◽

10.1155/2018/1092716 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Salvatore Cannella ◽

Roberto Dominguez ◽

Jose M. Framinan ◽

Manfredi Bruccoleri

Keyword(s):

Supply Chain ◽

Information Sharing ◽

Negative Impact ◽

Positive Impact ◽

Bullwhip Effect ◽

Sources Of Information ◽

Short Delay ◽

Demand Information ◽

Demand Variability ◽

The Impact

We investigate two main sources of information inaccuracies (i.e., errors and delays) in demand information sharing along the supply chain (SC). Firstly, we perform a systematic literature review on inaccuracy in demand information sharing and its impact on supply chain dynamics. Secondly, we model several SC settings using system dynamics and assess the impact of such information inaccuracies on SC performance. More specifically, we study the impact of four factors (i.e., demand error, demand delay, demand variability, and average lead times) using three SC dynamic performance indicators (i.e., bullwhip effect, inventory variability, and average inventory). The results suggest that demand error has a negative impact on SC performance, which is exacerbated by the magnitude of the error and by low demand variability scenarios. In contrast, demand delay produces a nonlinear behavior in the supply chain response (i.e., a short delay may have a negative impact and a long delay may have a positive impact), being influenced by the supply chain configuration.

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A Heuristic Simulation–Optimization Approach to Information Sharing in Supply Chains

Symmetry ◽

10.3390/sym12081319 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1319 ◽

Cited By ~ 1

Author(s):

KyoungJong Park

Keyword(s):

Supply Chain ◽

Information Sharing ◽

Bullwhip Effect ◽

Optimization Approach ◽

Significance Level ◽

Coordination System ◽

Total Inventory ◽

Asymmetric Flows ◽

The Impact ◽

Mean Variance

The sustainability of the supply chain is possible only if the profitability of all the tiers participating in that supply chain is guaranteed. The profitability of each of these tiers is ensured if information sharing as well as an effective and seamless coordination system are realized between the tiers. This process reduces the influence of an important risk factor known as the bullwhip effect. The purpose of the current study is to determine the necessary information sharing level to optimize the supply chain that has asymmetric flows of input and output values and to examine the effects of information sharing on the order fill rate (OFR) and total inventory cost (TIC) of the supply chain through analysis of variance (ANOVA) testing. In this work, the supply chain was optimized by using the particle swarm optimization (PSO) technique, with an objective function that assumes the maximization of OFR and minimization of TIC. The proposed method showed excellent results in comparing the mean, variance, and coefficient of variation. In addition, the method used the ANOVA test with a 5% significance level to verify the impact of the information sharing level.

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A Discrete Event Simulation Analysis of the Bullwhip Effect in a Multi-Product and Multi-Echelon Supply Chain of Fast Moving Consumer Goods

Pakistan Journal of Statistics and Operation Research ◽

10.18187/pjsor.v16i3.3088 ◽

2020 ◽

pp. 561-576

Author(s):

Ramsha Ali ◽

Ruzelan Khalid ◽

Shahzad Qaiser

Keyword(s):

Supply Chain ◽

Information Sharing ◽

Discrete Event Simulation ◽

Fast Moving ◽

Lead Time ◽

Discrete Event ◽

Bullwhip Effect ◽

Event Simulation ◽

Stochastic Lead Time ◽

The Impact

Timely delivery is the major issue in Fast Moving Consumer Good (FMCG) since it depends on the lead time which is stochastic and long due to several reasons; e.g., delay in processing orders and transportation. Stochastic lead time can cause inventory inaccuracy where echelons have to keep high product stocks. Such performance inefficiency reflects the existence of the bullwhip effect (BWE), which is a common challenge in supply chain networks. Thus, this paper studies the impact of stochastic lead time on the BWE in a multi-product and multi-echelon supply chain of FMCG industries under two information-sharing strategies; i.e., decentralized and centralized. The impact was measured using a discrete event simulation approach, where a simulation model of a four-tier supply chain whose echelons adopt the same lead time distribution and continuous review inventory policy was developed and simulated. Different lead time cases under the information-sharing strategies were experimented and the BWE was measured using the standard deviation of demand ratios between echelons. The results show that the BWE cannot be eliminated but can be reduced under centralized information sharing. All the research analyses help the practitioners in FMCG industries get insight into the impact of sharing demand information on the performance of a supply chain when lead time is stochastic.

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